Quantifying Patterns of Biological Diversity using Vertebrate Habitat Models

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Quantifying Patterns of Biological Diversity
using Vertebrate Habitat Models

• Frank A. La Sorte

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Acknowledgements

• Southwest Regional Gap Analysis Project Bruce
  C. Thompson, Kenneth G. Boykin, Scott Schrader,
  Robert A. Deitner.
• New Mexico Cooperative Fish and Wildlife Research
  Unit, New Mexico State University.

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Justification

• We require tools to quantify large scale patterns
  of biological diversity
• provide information for decision making on large
  scale environmental issues
• Adequately documenting these patterns is
  inherently difficult
• tremendous ecological complexity
• lack of time and resources

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Justification (continued)

• Trade off
• document large scale patterns quickly with
  limited precision, accuracy, generality, and
  predictability
• Gap analysis presents regional patterns of
  vertebrate habitat associations within their
  estimated ranges
• However, it lacks well defined methods for
• quantifying diversity
• decision making

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Objectives

• Concept of Ecological Diversity
• Dimensions and Components
• Quantifying Diversity Patterns within Gap
  Analysis
• Richness, Evenness, Distinctness
• Example 1996 New Mexico Gap Data
• New Mexico
• Tularosa Basin
• Applications for Decision Making

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Limitations

• Models are subjective, non-probabilistic, and
  static
• Accuracy unknown
• Level of error propagation unknown
• Variables highly correlated
• Qualitative (presence/absence)
• Limited to vertebrates
• Varying perceptions of scale by species
• Habitat is scale dependent
• No intra- and interspecific factors
• Ignores size and spatial arrangement of patches

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Ecological Diversity

• Units
• Grain
• Extent
• Diversity patterns
• Richness
• Evenness
• Taxonomic distinctness
• Landscape patterns
• Time

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Diversity Patterns
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Vertebrate Habitat Models

• Large scale biological and abiotic features, that
  can be represented cartographically, found within
  the estimated range of a species
• Combined with Boolean AND operators to create a
  binary grid (presence/absence)

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Gap Richness

• Definition quantity of vertebrate habitats
  within a unit area
• Sum all vertebrate habitat models

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Richness

• Does not predict presence, persistence, fitness,
  habitat quality, or biodiversity
• Interpretation of richness hotspots problematic
• Not consistent across taxa
• Scale dependent
• Biased towards marginal populations

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Gap Evenness

• Definition How evenly vertebrate habitat is
  distributed within a taxonomic level in a unit
  area
• Species as the unit of evenness
• Summarize evenness trend with principle component
  analysis

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Evenness Metrics

• Non-ordered or scalar metrics
• Ordered metrics
• Information theory (Rényi, Hill)
• Cumulative measures (Lorenz curve)

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Lorenz Curve
Lorenz Curve (0, 0) (1/s, p1) (2/s, p1
p2)(s-1/s, p1 p2 ps-1) (1, 1) Where the
proportions are ranked p1lt p2ltlt ps for s
groups Gini Coefficient (Area between the
Lorenz Curve and the line of equality) x 2
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Lorenz Curve
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Gap Taxonomic Distinctness

• Definition How taxonomically distinct vertebrate
  habitat is within a unit area
• Measured as the average taxonomic path length for
  each cell based upon a weighting factor (Clarke
  Warwick 1998)

• Path length weights (?)

Average path length
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Example New Mexico

• NM GAP 1996
• 100 x 100 m cells
• 346 vertebrate species
• 20 Orders
• 55 Families
• 195 Genera

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Example Tularosa Basin
Alamogordo
White Sands National Monument
Sacramento Mountains
9,369.2 km2
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Richness
Taxonomic distinctness
Evenness
Order
Family
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Correlation Matrix
Gini Family Gini Order Richness TDI
Gini Family 1 0.877 -0.199 -0.170
Gini Order 0.887 1 -0.073 -0.036
Richness -0.119 -0.073 1 -0.208
TDI -0.170 -0.036 -0.208 1
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Application to decision making

• Provides more detailed information on the
  patterns of diversity
• multivariate nature of diversity
• Use a combination of diversity components with
  other variables to
• more accurately represent gaps
• contrast sites based upon some criteria
• locate sites with desired characteristics

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Decision Making

• Assist in directing policy, research, management,
  and conservation efforts more efficiently
• Should not be the sole source of information
• Should be interpreted cautiously and honestly